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Hiring and Learning in Online Global Labor Markets

This discussion explores the influence of country of origin on online hiring in global labor markets. It examines factors such as laws, institutions, languages, skills, discrimination, and xenophobia, and analyzes the actions taken by potential employees to mitigate these effects. The research question focuses on the extent to which hiring decisions depend on country of origin and review history on freelancer.com.

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Hiring and Learning in Online Global Labor Markets

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  1. Hiring and Learning in Online Global Labor Markets Roy Mill (Stanford) Discussant: Rob Seamans (NYU) NET Institute Conference on Network Economics New York, NY April 20, 2012

  2. Research Question:How does country of origin affect online hiring? • Important and timely question • 25% of US jobs are potentially off-shorable (Blinder and Krueger 2009) • Not just software coding: Freelancer has categories for law, engineering, screenwriting, etc • How do cross-country differences affect off-shoring? • Laws, institutions, languages, skills • Discrimination/ xenophobia • What actions do potential employees take to mitigate? Discussion of Mill

  3. Research Question:How does country of origin affect online hiring? • To what extent do hiring decisions on freelancer.com depend on country of origin and review history? • Pr (Hired) = f(bid, #reviews, country of origin dummies) • Main area of interest: #reviews*country of origin dummies, given that employer is from US. • Comparison: US employer propensity to hire US employee versus US employer propensity to hire non US employee • 1st stage: bid = g(#reviews, country of origin dummies, exchange rate) Discussion of Mill

  4. Discussion of Mill

  5. Things to Consider • Work with other measures of cross-country difference • Political affinity measures (UN voting patterns - Garmaise and Natividad 2011); governance/institutional similarity (World Bank); language(%English-speaking) • Goal: understand why there are differences • Also would allow you to use more of your data • How else can potential employees win bids? • Certification (cost of freelancer.com exams are in US$) Discussion of Mill

  6. Things to Consider 2 • Lead with the first stage results: bid = g(#reviews, …) • Nice tie-in with existing literature on link between bids and feedback in eBay auctions (e.g.: Cabral and Hortacsu 2010) • Compare magnitudes to prior literature • Other potential first-stage dep vars (e.g.: #bidders) • To speculate on: • Role of intermediaries (Stanton and Thomas, 2011); • Competition between platforms Discussion of Mill

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